System and method for a distributed application and network security system (SDI-SCAM)

ABSTRACT

This document discloses the architecture and proposed application of a highly distributed network security system. Using a combination of intelligent client-side and server-side agents, redundant memory arrays, duplicate network connections, and a variety of statistical analytics, which are cleverly designed to anticipate, counteract and defeat likely strategic designs, behaviors and adaptations of these threats which may be intended to evade or even disable the network security system, this system serves to detect, prevent, and repair a wide variety of network intrusions.

Continuation in Part of application Ser. No. 10/693,148 and applicationSer. No. 10/693,148 is incorporated by reference.

This patent claims the benefit of Provisional Application 60/436,363

REFERENCES CITED

“Secure Data Interchange”, Herz, et al.

“System and Method for Pre-Screening Potentially Litigious Patients,Herz, et al.

BACKGROUND OF THE INVENTION

Computer networks today are as vulnerable as ever from unauthorizedintrusions by external entities. The increased complexity and variety ofcomputer systems in operation means that an even wider array ofintrusive strategies is possible, in turn requiring ever moresophisticated protective mechanisms.

Although simultaneous attacks are often launched against entirenetworks, most existing security systems are focused at the level of theindividual machine—ports are monitored for suspicious activity, incomingfiles are scanned for viruses, and user accounts are protected fromunauthorized access. Network-level security is much harder tocontrol—and it may take time for coordinated threats to be detected andcounteracted. For example, a virus may have several days to spread andattack individual machines before public awareness of the threatemerges, and even then it may take several more days for securityexperts to create and disseminate a countermeasure. In the first fewdays of such an attack individual system operators may not realize thattheir systems' problems are not simply localized disturbances, butrather a network-level problem, and it is during this window of timethat much of the damage is done both directly and indirectly byreplication and propagation across the network(s).

BRIEF SUMMARY OF THE INVENTION

This document discloses an architecture for a widely distributedsecurity system (SDI-SCAM) that protects computers at individual clientlocations, but which constantly pools and analyzes information gatheredfrom machines across a network in order to quickly detect patternsconsistent with intrusion or attack, singular or coordinated. When anovel method of attack has been detected, the system distributeswarnings and potential countermeasures to each individual machine on thenetwork. In a preferred implementation such a warning may potentiallyconsist of a probability distribution of the likelihood of an intrusionor attack as well as the relative probabilistic likelihood that suchpotential intrusion possesses certain characteristics or typologies oreven strategic objectives in order to best recommend and/or distributeto each machine the most befitting countermeasure(s) given all presentlyknown particular data and associated predicted probabilistic informationregarding the prospective intrusion or attack. If any systems areadversely affected, methods for repairing the damage are shared andredistributed throughout the network. The net impact of SDI-SCAM is thatevery machine on a network can benefit from security experience gainedat any other point on the network. A high and uniform level of securityis therefore assured to all systems attached to the network, and thissecurity is updated in real-time.

REFERENCE TO SEQUENCE LISTING, A TABLE

-   Title of the Invention—page 1-   Inventors and Addresses—page 1-   Continuation in Part of Application No. 60/420,754 and Application    No. 60/420,754 which is incorporated by reference.-   Conversion of Provisional Application No. 60/436,363-   Cross Reference to Related Applications—page 1-   Background of the Invention—page 1-   Brief Summary of the Invention—page 2-   Brief Description of the Drawing—page 3-   Detailed Description of the Invention—page 3-   Abstract—on a separate sheet of paper-   Claims—on a separate sheet of paper

BRIEF DESCRIPTION OF THE DRAWING

FIG. 1: Illustration of Potential Architectures

This FIGURE demonstrates some of the architectural features discussed,including (a) redundant memory within a given machine, (b) redundantconnections between clients and servers, (c) SDI-SCAM installed asprimary security system, (d) SDI-SCAM piggybacking on existing securitysystem, (e) direct client-to-client agent communications, (f) on arouter.

DETAILED DESCRIPTION

The basic architectural approach for SDI-SCAM is that each node of acomputer network is loaded with an agent capable both of ensuringsecurity at the locality of the machine on which it is installed, and ofcommunicating with other SDI-SCAM agents across the network. Becauseagent configurations are highly flexible, SDI-SCAM implementations canvary widely, running the spectrum from fully centralized (in whichSDI-SCAM agents on client machines communicate uniquely with acentralized server dedicated to processing security-related information)to fully distributed (in which each client agent is given the ability toprocess security information locally, and information is shared on apeer-to-peer basis).

Basic Network Elements

The preexisting elements of this network security system are themachines themselves. It is assumed that these systems, which act as thenodes of a network, consist of heterogeneous pieces of hardware runningdifferent sorts of operating systems. It may well be the case thatvarious security layers will already be in place.

Additional Hardware

In preparation for the installation of SDI-SCAM across a network, itwill often be desirable to upgrade existing machines with redundanthardware.

In the preferred embodiment, preexisting systems will be supplementedwith redundant memory systems that persistently mirror the contents ofthe primary memory banks. When a computer's primary memory is corrupted(as can happen during a viral attack), it can be completed, cleared andreset with a pre-corruption image from the backup.

A further redundancy can be built into the network connections that linkthe local nodes to SDI-SCAM servers. For example, a computer thatnormally operates through land-based optic lines may be given anadditional wireless connection through a satellite system.

An expensive, but preferred, architecture is to connect each SDI-SCAMagent through a fully isolated network that operates independently fromthe network on which the protected system resides. Thus, the SDI-SCAMagent will remain in contact with the security network even when thesystem it is supporting is under a sustained or unusually intenseattack.

SDI-SCAM Agents

An agent is an entity that can be loaded onto any node(s) of a network,and which in this case is assigned responsibilities related to systemsecurity. Note that the construction of a given agent can vary widely,as it can be implemented through software, through hardware, throughhuman interaction, or some combination thereof. In the preferredembodiment of SDI-SCAM, every machine linked into the system is loadedwith an SDI-SCAM agent. Agent responsibilities include the following:

1) The collection of traffic data—among other things, each agentobserves the packets being routed through its local system, observesevery file transmission, monitors every user action, and logs everyrequest for access.

2) The ability to communicate with other SDI-SCAM agents—each agent hasthe ability to communicate and exchange information with other agents(although the content of this information and the agents with which itis shared may be controlled, as will be discussed later). In normal use,a remote agent will send filtered traffic information downstream. Whenother agents detect potential security threats, warnings will passupstream back to the remote agent.

3) The maintenance of various protections—On a continual basis, SDI-SCAMagents send and receive warnings and potential countermeasures relevantto whatever network risks are the most likely at a given time. Forexample, if a computer virus is detected at one node on the network, thelocal agent will immediately communicate a warning to all other agentsin its contact neighborhood. If an attack is especially bad, the agentwill have the ability to swap into the backup memory or contact otheragents through alternative communications lines.

Note that SDI-SCAM can operate either as a standalone security system,or as an additional layer that subsumes (and takes priority over, incases of conflict) existing security protocols.

4) The ability to repair damage—Even after a node is known to have beenattacked, the SDI-SCAM agent can be given access privileges such that itcan aid the system administrator in controlling and repairing whateverdamage has resulted.

The ability to scan collected data traffic for patterns consistent withthreats—In many configurations, SDI-SCAM agents share their trafficinformation with a dedicated SDI-SCAM server capable of gathering andsifting through the entirety of the traffic data in order to detectpatterns consistent with a network attack, be it related to a hacker orto a new virus. Certain traffic events, which individually may bemistaken as simple anomalies, may become more apparent when the totalityof a network's (or multiple networks) traffic is considered on a macroscale.

Notifying system administrators in the event of certain probabilisticattributes exceeding certain levels—The system's implementation of aBelief network (as herein disclosed) may also be used to determine underwhat overall conditions of probabilistically determined and descriptivevariables it is advantageous to notify the system administrator. Thesevariables can be based upon the predicted likelihood for the system tosolve the problem, prevent certain types of problems, undesirable eventsand/or quantified degrees thereof from occurring or manual/or manuallyadaptive rules may prescribe threshold settings for some or all of thesekey variables. Among other situations, the system administrator may benotified or alerted in cases in which patterns detected may be onlyslightly suspicious according to the standard screening methodology,however, are consistent with SDI-Scam's best estimated simulation modelfrom its distributed agent sources of how a threat might emerge, e.g.,by mutation and re-emergence, e.g., after initially being defeated bySDI-Scam.

Meta-data associated with the accessor like a watermark which can alsobe embedded in code which contains digital credentials of the user (wecan also apply here the system described in co-pending patent entitled,“System and Method for Pre-Screening Potentially Litigious Patients”)however, which in the present application incorporates the use of“potentially” rogue, irresponsible, or destructive individuals as perthe types of associated predictive attributes from criteria as disclosedin the presently preferred embodiment. The code cannot be tampered withwithout interrupting the watermark. A more general term for this“invisible” code sequence, which appears random to a would-beinterceptor is “embedded code”. Typically the embedding is done in amuch larger nonsense message to apparently random patterns (in as muchas the application code would already be encrypted) this nonsensemessage content may not be required. Also, it can be associated withfunctionally defined portions of the code, which pre-approve certainbehaviors. The system could also be based upon willingness of theaccessor and/or code which s/he writes to statistically pseudonymize andprofile the user with that of the patterns/types, etc. of code s/he haswritten in the past, thus predicting even without explicitidentification who is the likely author and what s/he is like, i.e.,what is the statistical probability distribution of the individual toeach of a variety of previously known identities based upon codemorphological characteristics, functional behavioral features, humanbehavioral features (e.g., if it is accompanied by a human attack).Pseudonyms and resolution credentials may be useful to authenticate thebasic intent and MO of the author of the code while use ofcryptographically interoperable pseudonyms, i.e., multiple unique butsingle identity aliases which are linkable to that single author only bySDI-SCAM for its security analytical purposes and under prescribedconditions (as data disclosure policies) as dictated by that author.Pseudonyms may be used to insure the same level of anonymity of theauthor as uncredentialed code. This approach could, of course, either beimplemented as a local protocol (i.e., existing applications,application updates and new applications could all possess thesecredentials verifying/certifying that the present code was written by anindividual who has been certified by a trusted certification authorityas being non-malicious. This approach and the above pseudonym basedidentity protection scheme, while applied in this case to theapplication of software security are disclosed in detail for theapplication of identity protection, data privacy and security from rogueindividuals interacting on communication networks such as the Internet.These relevantly related techniques are well described in the parentcase as well as U.S. Pat. No. 5,754,938, entitled “Pseudonymous Serverfor System for Customized Electronic Identification of DesirableObjects”. Within a typical context this type of code certificationshould be impervious to a “man in the middle” attack. Such embeddedmessages (or in a similar cryptographic variation, “fingerprinting”) areinherently effective for the security application proposed inasmuch asany rogue code which a system attacker would attempt to insert into acertified application or communication or other communication containingexecutable code would contain within its sequences continuous portionswhich do not contain the embedded credential-based sequences. Likewise,in case the would-be man in the middle attempted to remove certain data,(e.g., credentials or functional application code) the fingerprintingtechnique would recognize the specific extracted code segments. Thisexact same problem can be solved alternatively another way in which theprimary objective is to transmit data containing a message the existenceof which is not possible to be detected by a would be “man in themiddle” attacker. In the example approach in which a content bearingmessage is embedded or fingerprinted into the application code (or lessdesirably in an associated larger message), the message can only beidentified by the recipient (the local SDI-SCAM agent) who may also besimilarly hidden or “steganographed” as with the originally sent message(in order to verify receipt of the message by the authenticatedrecipient. There may exist in this content bearing message a variety ofuseful credentials incorporated therein including but not limited tocredentials approving both authenticity, untampered state andauthentication of the sender and/or author as well as proof of certified“good intent” on the part of the code author. The technique for insuringthat the embedded sequences are completely undetectable, while at thesame time being diffusely spread throughout the code is typicallyperformed by using encryption techniques (e.g., pseudo-random sequences)to determine the positions of the sequence bits within the remainingcode in order to thus pass a message to the recipient (the localSDI-SCAM agent) containing the credentials and potentially the messageof the coordinates of the associated meaningful sequences, such that allof these content bearing sequences appear among the remaining code asrandom noise, including the portion of the message containing theencrypted coordinate data of which coordinate bits possessing thetotality of the embedded or fingerprinted message can be found withinthe application. Alternatively, this message containing the coordinatelocations of where to find the meaningful bits containing the contentbearing message may be embedded within a larger message which itselfappears to consist entirely of noise (which in and of itself lends thesecurity of the embedded or fingerprinted message contained therein).The primary hurdle in this case is to enable the recipient to be privyto certain data, which is not known to a would-be “man in the middle”attacker namely where to look for the message, i.e., the coordinates ofthe meaningful data constructing the message. This “shared secret”between the sender and the receiver could be conveyed to each partyinitially by a (one time) physical distribution (e.g., contained withinan application if it is physically distributed, such as on a disk, orvisa vie the OS or CPU, etc. In one variation in which the disseminationof this message needs to be performed on a network wide level (or grouplevel) the shared secrets may be physically distributed, once to allparties in a group and subsequently, all parties would be able toinstantly initiate communications with the security guaranteesachievable through the presently proposed methodology. This one timesingle distribution of data sets enabling these group levelcommunications is disclosed in co-pending patent application entitled,“Distributed Shared Sets”.

Finally, it will be sufficiently obvious to one skilled in the art thatthe presently proposed methodology has numerous potential applicationsin cryptography and data security and thus the means for distributingdata coordinates to a recipient of a steganographed message forconveying (and if desired reciprocally confirming) a message is in noway limited to messages, containing credentials and authenticationcertificates about an author and/or sender. For example, the presenttechnique could be very prudently employed as a means to distribute andreplenish shared set keys within the context of the above referencedco-pending application. It may also protect against man in the middleattacks against distribution of private keys in Pki protocols.

SDI-SCAM Network

There are multiple network morphologies possible. Major configurationsinclude the following:

-   1) Local network: SDI-SCAM enabled machines may form a local    network, such as a LAN or WAN. Gateways to external networks (such    as the Internet) can be fully controlled through SDI-SCAM enabled    routers.-   2i) Open network: On the other hand, SDI-SCAM enabled machines can    be connected directly to outside systems (such as a desktop system    connecting through a generic ISP), but which maintain communications    with a chosen neighborhood of other SDI-SCAM enabled machines.-   3) Centrally organized networks—in this configuration, thinner    SDI-SCAM agents are placed on individual nodes; these agents    continue to be responsible for direct security and repair, but    transmit gathered traffic information to central SDI-SCAM servers    containing dedicated hardware and software capable of swift and very    in-depth analysis of the gathered information.-   4) Distributed networks: in this configuration, each SDI-SCAM agent    shares the responsibility for traffic data analysis and the    generation of preventative measures with other agents. A    peer-to-peer morphology would work well in this case.    Inter-Agent Communications

Although there is clearly a benefit for agents to fully pool allinformation, it may be desirable to control both the content shared andthe partners with which a particular agent is allowed to interact. Theseparameters can be set at the local level according to users'preferences.

SDI-SCAM agents may in fact negotiate with each other depending on thevalue and sensitivity of particular information, as well as the value ofany likely synergies between them. Multiple agents may meet in virtualinformation sharing marketplaces (the methodology detailing the designand basic formulas for such an exchange is provided in the co-pendingpatent application, “Secure Data Interchange”).

Another level of security can be gained through the exchange ofobfuscated, but still valuable, information. Such randomized aggregateswould allow systems to share fundamentals without revealing details oftheir particular data (for example, agents could share times ofattempted log-ins without revealing the associated user ids and failedpasswords). The use of randomized aggregates is discussed in the sameabove referenced co-pending patent.

In more complex realizations of this system, associated groups of agentsmay form coalitions, with information shared freely internally, butshared with conditions externally.

A further feature is that communications between agents need not beperfectly symmetric—in other words, different agents may send andreceive different sorts of information. This might apply, for example,to a centrally organized SDI-SCAM network: outlying agents would have noneed to transmit detailed traffic data to each other, but would rathertransmit it directly to a central server. The central server mightcommunicate with other central servers, in which case it would transmithigh-level information relevant to the processing of the entirety of thetraffic data; on the other hand, when communicating with outlying nodes,the central server might only transmit simple virus protectioninstructions and metrics which are substantially devoid of any datawhich suggests what types of information, attacker strategies orapplications are running on other nodes on the system which are outsideof the network of nodes and which are currently trusted by the nodesfrom which the centrally collected and processed data had been acquired.

Furthermore, there may be an additional or alternative approach toguaranteeing absolute data security at a local network or machine levelwhile enabling maximal or complete harnessing of all of the statisticalknowledge, which is present across the entirety of the network. In thisapproach it may be possible to operate SDI-SCAM or certain particularlysensitive portions of it with its multiple agent architecture as asingular trusted, yet distributed multi-agent system. In this variation,all of the locally performed (or assigned agent functions are assumed tocontain sensitive data belonging to external third parties and thus allprocessing activities, data communications with other agents or thecentral SDI-SCAM server occurs within a secure trusted and untamperableenvironment such that the only knowledge ultimately accessible by anygiven agents, associated local server or network on which it physicallyresides may be the collection of executed functions which are performedby the local agent on behalf of the SDI-SCAM to protect the local systemas herein disclosed.

The order and way in which agents communicate with each other may behighly conditioned on the particular nature of a given system. Criteriainclude (but are not limited to) the following:

-   -   overall vulnerability of a system.    -   importance of the system to the integrity or functioning of a        network    -   sensitivity and value of the data stored on a system    -   probability that the system has already been compromised or        damaged    -   characteristics of the network traffic going to and coming from        the system    -   overall importance of a system to a potential or identified        hacker or specific system subcomponent. This may dynamically        change from moment to moment and is predicated by a        probabilistic estimate determination variable of the intruder,        whether autonomous or human and/or by human expert based        estimates who are ideally familiar with local competition (or        enemies) and broad knowledge of what types of knowledge on the        system would be most of interest to which other entities or        individuals and for what reason. If an individual is        specifically identified this statistical model may further        borrow and integrate techniques disclosed in co-pending patent,        “System and Method for Pre-Screening Potentially Litigious        Patients, Herz, et al

Updates and communications between agents (termed “polling”) may bebased on schedules or on circumstances. For example, a remote agent maybe updated with new antiviral software once a month; however, if anyother node on the network is attacked, the schedule is suspended and animmediate update is performed. Certainly even if an attack which, forexample, has only begun to occur or which has not even positively beenconfirmed as yet, triggers SDI-SCAM's system alert feature, other nodeson the network most preferentially/urgently those which are physicallyproximal or in other ways similar may also be put on alert status andSDI-SCAM's repertoire of protective features may be triggered so as tobegin operating at a heightened level of defensive activity. Asindicated there may be a range of different system defense levelscorresponding to a decreased probabilistic likelihood of a threat andthe likely severity thereof should this threat exist. Local systemadministrators are notified appropriately as well. Determining thelikelihood that a threat upon a particular node or network will also becarried out against any other given node can be predicted by suchvariables as commonalities at an organizational or strategic level, datacommunication occurring there between, commonalities in the existing orperceived data on applications contained or functional objectivesachieved upon that node, presume interest level that a potentialintruder of the attacked node or network may also have with the othernode, etc.

Polling priority may be based on calculated likelihoods: for example, ifvarious factors indicate that the probability is high that a remote nodehas been infected by a particular type of virus, the central server maybe put into immediate communication. Polling priority will also dependon the nature of the nodes and the way in which their agents have beenseen to communicate. U.S. Pat. No. 5,754,939, entitled “System forGeneration of User Profiles for a System for Customized ElectronicIdentification of Desirables Objects” may be used as the basis foroptimizing the way in which polling is performed.

Illustration

See FIG. 1 for an illustration of some of the configurations discussedhere.

Analytics

Given the number of different security objectives, as well as the numberand diversity of possible agents and network configurations, a fairlybroad range of analytical tools are employed by SDI-SCAM. They include,but are not limited to, the following major categories of analysis:

Methods to Detect and Classify Direct Intrusions

Direct intrusions are attempts by unauthorized entities to enter aparticular system, either over a network or through local terminals.These can range from fairly unsophisticated attacks (for example,teenage “script kiddies” using standard public domain software to scanfor open ports across a list of target IP addresses), to extremelyskillful attacks that are focused on a very particular target (as mighthappen during corporate espionage).

Since SDI-SCAM agents are able to dynamically monitor and analyze aswell as control all in-going and out-going traffic, they are in a goodposition to detect and counteract such attacks.

1) Attack Patterns Consistent with Previously-Observed Patterns Acrossthe SDI-SCAM Distributed System.

Each SDI-SCAM agent has access to a shared database that contains thesignature patterns of previously observed (as well as verified) attacks.The likelihood of these events having been actual attacks may beprobabilistically estimated so as to optimize the precision of SDI-SCAMdetection/diagnosis as well as countermeasure deployment system modules.Such patterns might include the use of a particular password list,log-ins at particular time intervals or frequencies or times, log-insfrom suspect IPs, (and/or combinations thereof) constitute a few of thestraightforward examples.

If such a pattern is detected, the resident SDI-SCAM agent may opt todeny all entry to the IP of the incoming log attempts, or it may opt fora more sophisticated defense, such as opening a “honey pot” trap, avirtual space that simulates the environment of the system that is beingprotected. The hacker, believing that he has actually broken into thesystem, can then be monitored by SDI-SCAM, as his behavior might givesclues to his location, identity, and motives and incriminatory evidence,if desired. Assuming the hacker has learned (or possesses) enoughknowledge about the system to detect “honey pot” traps it isadvantageous and precocious to possess at least equivalent knowledgeregarding SDI-SCAM to possess at least equivalent knowledge regardingits own environment and to be able to enable the system administratoraccess to that knowledge as well as (via SDI-SCAM) knowledge known orsuspected to exist within a probabilistic context regarding the hackeror threat and its strategy and/or this knowledge may be acted uponappropriately by SDI-SCAM in automatic mode. Invariably all countermeasures (such as honey pot traps) used by SDI-SCAM can be used to theadvantage of the hacker if s/he is aware of the strategy of SDI-SCAM tomonitor, model, locate in order to ultimately catch him/her.

2) Utilizing Data Modeling to Adaptively Learn and Recommend AppropriateCountermeasures

Implementation of practically viable automated countermeasurescrutinization and pecommendation scheme is quite achievable

a. If the conditions/parameter triggers are simple and unambiguous,

b. If the system administrator is notified and able to intervene whileexploiting the system's analytical knowledge and system-generatedrecommendations and scrutinies by the system on behalf of his/her chosenresponse decision. In the ideal scenario, because rogue attacks arecapable of performing increasingly effectively against system securityprotections (in addition to being more sophisticated and expeditious)and especially with regards to leveraging the system's own abundantlycapable resources, it may be ideal as a complementary measure tobuilding redundancy into the system resources in the interest ofexpediency of decrypting a counter measure, to also immediately respondin automatic mode, then solicit the active, albeit system-guidedintervention of the system administrator whereby more significantdecisions can be perhaps more confidently and prudently executed (e.g.,whether or not to delete potentially corrupted files/portions of systemdata at the server or network level, whether to guarantee a certainportion of the network but allow certain essential functions to continuefor the time being without code exchange, whether or not to attempt toinfect the hacker's machine (or analysis code into the virus itself)which may provide additional detailed information as well, etc.

3) Novel Attacks

In some cases, attacks will follow completely new or novel patterns.

Such attacks can be detected in different ways. One solution is toconfigure a Bayesian network to constantly gauge the probability of anongoing attack by monitoring network traffic activity (thisconfiguration can be done by human experts and/or through machinelearning techniques). A variety of factors can be extracted from thenetwork traffic across all SDI-SCAM agents in the local network—forexample, the number of failed log-ins, the identities and IP addressesof those users attempting to log in, the importance, sensitivity or“value” (more specifically “perceived value”) of particular target filesor contents potential adversarial entity or prospective hacker, etc.These factors are fed into ongoing probability calculations, which maytrigger a system-wide warning if a certain threshold is surpassed.

Keystroke monitoring virus must be mentioned since it is impervious toNorton, etc.

For example, suppose a ring of corporate spies tries to hit a company'snetwork simultaneously. SDI-SCAM agents across the network will reportthe use of unauthorized passwords originating from the same IP or IPs towhich associations have been constructed via SDI-SCAM based uponhistorical statistics if the probabilistic likelihood of such eventsoccurring independently might be so unlikely that the Bayesian networkwould immediately increase its estimate of an ongoing attack.

4) Attack Warnings

Note that in all cases, when an attack is suspected the residentSDI-SCAM agent will immediately alert all the other SDI-SCAM agents inits network neighborhood, sharing all traffic information relevant tothe on-going security issue. Such warnings will include informationrelated to the particular nature of the problem, in particular theprobability and nature of the threat (for example, communication with anunsecure system, access by an authorized user, reception of potentiallyinfected files, etc.).

When an on-going attack is announced, SDI-SCAM agents receiving thisinformation may opt to increase the security levels of their ownsystems. For example, users may be required to telephone at the time oftheir log-in to verify location (through caller ID) and voiceprint.

Methods to Detect and Classify Viruses or “Trojan Horses”

Includes ideas of analyzing origins, possible paths of transmissionacross sites, origins, etc. types of files (e.g., particularlyvulnerable or vulnerable origin site), how to use this data to make avulnerable application, Trojan horse attempt impervious, make rogueness,crypto query, even rewrite code.

Another vector of attack is through viruses (which are oftenunauthorized and malicious programs attached to files, email, ordocuments) and trojan horses (seemingly innocuous programs that containhidden programming capable of causing damage).

Code Analysis

The conventional viral detection methodology is to scan the code (in thecase of executable modules) and macros (in the case of smart documents,such as those generated by Microsoft Word) for patterns that havepreviously been associated with viruses or malicious programming.

SDI-SCAM maintains up-to-date records of all known viruses and checksall incoming files (and periodically, all stored files) against theserecords. A match indicates that a file is potentially infected—the useris alerted of the danger and automatic defensive measures may be setinto motion.

Behavioral Analysis

SDI-SCAM monitors all processes for behavior consistent with viralinfection. For example, a program that is observed to open and modify awide range of heterogeneous files, which accesses the mail system'saddress folder, which aggressively propagates copies of itself, whichengages in recursively redundant actions whose objective is designed toachieve no useful purposes or frequently which aggressively/repetitivelygenerates or obtains data files in order to propagate inordinatelyvoluminous and/or large files (possibly including itself) resulting inbursts of traffic thus overloading valuable network transmissioncapacity) which performs similar recursively redundant actions resultingin consumption and overloading of valuable processing capacity, whichmodifies or mutates its own code (and/or behavior) or which opensunexpected communication ports with outside entities will be flagged asa potential threat unquestionably SDI-SCAM's highly distributed datatraffic monitoring and behavior and code analysis facilities as acombined approach give it a marked and compelling advantage in rapidlyanalyzing those behavioral patterns and characteristics most commonlyassociated with a rogue code such as viruses, Trojan horses, worms, etc.whose tell tale signs could not be identified nearly as expeditiously asthat of SDI-SCAM's distributed agent monitoring architecture. Suchcommonly occurring signatures which SDI-SCAM's distributed Bayesianmethodology is particularly well suited includes those patterns of selfreplication and dissemination through address books, email, web browsingsessions, etc., as well as the co-occurrence of identical or relatedpatterns of behavior and code sequences in conjunction with thesereplicating and self propagating patterns as observed only on a networklevel. Certainly part of this behavioral analysis may encompass attemptsby SDI-SCAM to classify the identity or type of virus based upon all ofthe above observed characteristics as well as attempting to extrapolateits high level objectives and associated executable rule sets based uponits behavioral patterns associated with the conditions/variables of theenvironment which it has encountered, the data which it has likelyaccessed, the actions, events and countermeasures to which it has beenexposed, the code within which it has likely been embedded, etc.Although it may be difficult to delineate rogue from innocuous code itis certainly within the scope of capabilities of SDI-SCAM to utilize allof the available data, both behavioral and code sequences in order toattempt to reverse engineer the code for the purposes of both predictingits future behavior, likely past behavior and high level objectives. Forexample, SDI-SCAM could replicate the code inside of an environmentwhich is quarantined from the network, but which is a replica of thenetwork or a portion thereof. SCI-SCAM could then monitor how the codebehaves in this simulated environment to the actual one as well asobserving its response to targeted stimuli, which may, for example,provide opportune conditions for the most likely rogue actions to beperformed. This analytical procedure may be performed in response to apredicting statistical model (designed to predict the code's behavior)when a decision tree could be used to dynamically select the set offunctions to be executed which based upon the same model are correlatedand then predicted to elucidate responses on which are the mostoptimally revealing, reveal the most revealing which is needed tocomplete the construction of this data model for the codes for beingable to predict the code's behavior across a wide array of conditions,actions, software and data to which it may ultimately become exposedwithin the entirety of network(s). In depth analysis of potentiallysuspicious code although challenging as it may be could potentiallyprovide system level insights into how to best respond to the potentialthreat and if mandatory the nature and aggressiveness of countermeasuresto be taken or recommended to the appropriate human system securitycounterpart.

The user will be alerted, and if he confirms that the program isoperating outside of expected parameters, or if the user does notrecognize the program, it is taken offline until it can be examined indetail by an expert.

Dead-Ringer Analysis

Although not currently a threat, it is likely that infectious programswill be able to simulate the behavior of human users. A suite ofbehavioral response tests can be developed to detect and counteract suchentities, e.g., a probabilistic model based upon other previous threatsin the statistically similar characteristics (including behavioralcharacteristics and certainly those determined to be the most likely tobe the same. And/or queries which may be required of the “user” to beanswered correctly or to perform a task (e.g., compose a block of texton the basis of a query) in order to proceed could be solicited of theuser which are crafted such that an emulating virus would likely failsuch query procedure. Moreover, Natural Language Processing methods canbe used to analyze outgoing text for irregularities consistent with anon-human origin. It is possible that in a similar fashion, that, intheory very smart emulations of existing code could be manually or evenautomatically on the fly created which emulates in many respectsexisting “good code”, but which actually is designed for maliciousobjectives or, for example to take over control of the good code orreplace it with the rogue version. As additional attributes of thesystem, refer to the above-mentioned co-pending patent, “System andMethod for Pre-Screening Potentially Litigious Patients, Herz, et al todetermine probability and degree of ill motive of individuals of mostlikely suspicion (if such suspicion is high enough to be of reasonableconcern). Typically common suspicion of particular individuals can belinked to unscrupulous employees (present or former), disgruntledemployees, disgruntled spouses of key persons/owners (e.g., changingfiles, information release, etc. to embarrass or defame the person or tofeign a verbal or tactical attack on a friend, associate or colleague.Such “suspects” could also include trusted partners who may be confidedwith knowledge of the existence of unique information which could be ofinterest directly or could even help or strengthen that party in itsbusiness position with its “trusted” business partner.

As additional attributes to the system, refer to co-pending patent,entitled “System and Method for Pre-Screening Potentially LitigiousPatients, Herz, et al to determine probability and degree of ill motiveof individuals of most likely suspicion (if such suspicion is highenough to be of reasonable concern). Typically common suspicion ofparticular individuals can be linked to unscrupulous employees (presentor former), disgruntled employees, disgruntled spouses of keypersons/owners (e.g., changing files, info release, etc. to embarrass ordefame the person or to feign a verbal or tactical attack on a friend,associate or colleague.

Such “suspects” could also include trusted partners who may be confidedwith knowledge of the existence of unique information which could be ofinterest directly or could even help or strengthen that party in itsbusiness position with its “trusted” business partner.

Control of Triggers

If the probability of an infection is deemed to be high, SDI-SCAM maycontrol the generation of events that could potentially trigger thereaction of a resident virus. For example, if a bank suspects that acorporate virus has infected the system, all transactions may besuspended until the virus is cleared. Otherwise, the action of a userrequesting an on-line transaction (thereby releasing his personalpassword to the system) may trigger the virus into capturing andre-transmitting his personal information.

Tracing Threats Back to Their Original Source

In traditional system security techniques this objective is highlydesirable and yet extremely difficult. Nonetheless, SDI-SCAM'sfunctional features lend themselves quite well to the design of certainparticular types of applications, which can be useful in addressing thisparticular problem. For example, the following example applications maybe herein considered:

1. “Infecting” the hacker's machine (or the virus) with a virus, whichlogs and/or conveys back to the SDI-SCAM agent the location, behavior,files infected as well as all IP addresses of the machines in whichthese files reside. This approach is likely to work provided that theimplanted virus by SDI-SCAM is not recognized by standard virus scanningsoftware or other IDS systems and assuming that the receiving machine isnot programmed to block any outgoing messages. Thus, the success wouldbe determined in part by the effectiveness of the virus to take controlof the adversary's (or rogue virus containing) machine. This type ofdirect analysis will both enable preemptive alerts of exactly where thevirus may be spreading to other machines and/or networks as well asprovide valuable statistically confident data as to the function,behavior, data or code affinities and behavior in response to infectionof the same as well as epidemiological characteristics which could beextremely valuable as to anticipatory determination and qualification ofthe associated threat on other machines, as well as the most appropriatecountermeasure each local agent should implement or receive in response.Certainly, this approach could be useful for viruses, which possessparticular rapidly proliferating characteristics, rapid infliction ofdestructive behavior (for example, one could imagine the behavior ofmore sophisticated viruses which might proliferate themselves asredundant messages so as to rapidly overwhelm network capacity and/ormemory processing and/or implement parallel strategies).

This approach could also enable SDI-SCAM to model not only futureepidemiological characteristics of rogue software but also that of postepidemiological behavior (which machines or networks were likely to havebeen infective previously based upon presently known epidemiologicalcharacteristics and the devices/networks which are known to be andprobabilistically suspected of being infected by the same virus (ormutated variant thereof). Certainly reconstruction past, present andfuture behavior in this regard could be relatively easy to perform forworms that may require access to ISP server logs for other variationswhich may use email and web server connections as a medium oftransmission. Or a protocol may allow for the existence of a latenttracking virus to reside within all machines which can be, in the caseof significant probability of a threat in and among a network communityor otherwise “group” an excessive probability of a threat, the trackingvirus may be remotely activated by a multi-casted activation messageoriginating form a core (or root) server.

2. Use of SDI-SCAM Architecture for Application Level Security

It will be increasingly important in the future for many of thefunctions of SDI-SCAM as implemented within the context of its presentlydisclosed distributed statistical analytics to be implemented not onlyat the level of a distributed network security system but also at theindividual application level. That is to say that SDI-SCAM agents could,in addition to the above described system level implementations, alsoimplement their various functions for data collection, analysis, andcountermeasures at the application level as well both to implement otherapplication level security protocols as well as incorporate into thestatistical analytical scheme probabilistic attributes regarding thebehavior functions, etc., of such rogue code within the context of theparticular relevant applications in need of protection, albeit using thesame distributed adaptive modeling and countermeasure response protocolsdescribed herein in comprehensive fashion.

Methods to Detect Tampered Files (Semantics and Content)

It is sometimes the case that intruders, rather than destroying orremoving files, will simply alter them in potentially malicious ways.For example, students may attempt to hack into their school system inorder to change grades, or a more advanced hacker may attempt to breakinto a bank to fraudulently increase the balance in his account, intotax or criminal record databases in order to change tax liabilities,records of property ownership or criminal records, into professionalboard's databases in order to change licensure status. Similar tamperingmay occur to files whose contents may relate to the hacker (e.g.,employee files of present or past employers). Malicious code may, intheory, perform all of the functions that a human may perform, perhaps,however, potentially even more unobtrusively and elusively in that itmay be more difficult to trace and flag than a human if the code is verysmall, robust and capable of focused but sophisticated emulations oflegitimate applications and users.

In addition to the above suggested techniques for use in tamperingdetection and ultimately prevention (or even tracing the origins oftampering attempts), there are other straightforward IDS-basedapproaches by which such attempts could be countered (and could evencomplement the above safeguarding scheme, for example, in terms of beinga default detection scheme and/or in corroboration of the presumedintegrity of credentialed individuals). Thus the following IDS-basedalternative technical approach is also provided as well. The localSDI-SCAM agent maintains logs that detail the general characteristics(size, word count, hash code) of all documents on the system. The timeand circumstances of any changes are cross-checked against averagetraffic patterns for that particular system. Hence, school recordsaltered at 3 am (in a district where all school secretaries workedstrictly from 9 am to 5 pm) may be flagged as potential objects oftampering.

Tampered files will sometimes show a marked change in writing style ortechnique. Natural Language Programming (NLP) techniques may be used todetect such changes. Certainly in the event of these suspiciousactivities and other conditions, it may be advantageous to retain notonly the associated statistical data (as the SDI-SCAM doesautomatically) but also details regarding the events. This could, forexample, be later analyzed by humans to compare with other similarsuspicious patterns also captured in detail in order to attempt toidentify patterns, more subjective signatures, or hall marks which maynot be able to be performed automatically (such data may also be usefulfor potential legal evidence).

Methods to Detect and Classify Untruthful Commercial Messages

Untruthful messages represent a more traditional kind of deception—thetechnology of the delivery is not damaging, rather, the content of themessage itself is untruthful and may prove harmful if taken at facevalue by the receiver. A good example of this is the “Nigerian Scam,” awidely disseminated email that purports to be authentic, asking thereceiver to give the sender access to an American bank account inexchange for great financial reward. The result, of course, is that thereceiver ends up being defrauded of large amounts of money.

1) Cross-Checking Content Against Known Hoax Documents

SDI-SCAM maintains a database of questionable messages and uses naturallanguage programming-based techniques to compare incoming messages withpreviously logged deceptions. Thus, when a suspicious message isdetected, the receiver may be sent a secure attachment by SDI-SCAM withan email stating that there is a high probability that the mail isuntruthful, and indicating pointers to web pages that discuss thatparticular deception. If a user is nonetheless deceived by such amessage, the local SDI-SCAM agent may be alerted. It will transmit thetext of the novel message to a security database, allowing every otherSDI-SCAM in that network neighborhood to be aware of the danger. In sucha case, the agents may retroactively warn users by scanning old emailsand alerting receivers of possible deception.

Certainly in such an event, autonomously implemented counter measuresmay also be performed if appropriate as a defensive or evasive action ordeterrent, e.g., if a pass code was inadvertently sent out (and it wasnot blocked by the system) the pass code could be automatically changedor temporarily frozen or if a personal bank account or credit cardnumber were sent out in a suspected inappropriate context (againassuming it was not blocked at the source by the system), the accountcould be automatically temporarily frozen and the number changed or (forexample) the account automatically set up as a honey pot trap to acquirejust enough information about who the suspect entity is in order tocatch him in an inappropriate act of fraud or deception.

2) Predicting Possible Hoax in Novel Message

In cases where a message is not closely correlated with known hoaxes, itis still possible to analyze (using natural language processingtechniques that are currently well known to the art) the content of themessage and flag any suspicious content:

-   -   the content of the message can be cross-checked against recent        news stories discussing hoaxes.    -   the names and return email addresses of the incoming mail may be        checked against those of known hoaxsters.    -   Automated semantic analysis of the message may be performed for        language consistent with persuasion or appeal to greed (or other        weaknesses). This analysis is performed on the basis of adaptive        rules which may be updated with feedback.    -   The identity and personal profile of the receiver may be        correlated with the characteristics of known victim groups. For        example, messages sent to rich elderly individuals may be given        additional scrutiny.    -   The purported identity of the sender can be checked against the        path of the email. For example, a message claiming to be from        the IRS should trace back to an official government system.    -   A probabilistic assessment of the likelihood that the sender is        fraudulent may be performed through a modified version of the        system described in the co-pending patent application entitled        “System and Method for Pre-Screening Potentially Litigious        Patients, Herz, et al in which the system's probabilistic        determination of predictive attributes relevant to an        association with fraudulent, unscrupulous or disruptive behavior        (in an on-line context) is performed—of course, the sender if        self identified may also be fraudulent. The on-line sender just        prior to the first receiving node on the system may also be        analyzed which is a reasonably reliable tracking means if        SDI-SCAM is a ubiquitous protocol (e.g., for patterns of being        the origination node for previous problematic messages and/or        the techniques disclosed in the same co-pending patent        application, “System and Method for Pre-Screening Potentially        Litigious Patients, Herz, et al” may probabilistically predict        the suspicion level of an individual(s) or organization(s)        associated with that sender as being linked to other scams        and/or other illegitimate or questionable activities. Related        techniques may use other advanced customized semantic analysis        and/or adaptive rule based/statistical techniques (preferably in        combination) in order to estimate the degree of potential        harmfulness of the content.    -   The content may be corroborated with the content of known and        trusted documents, e.g., through the use of content matching        techniques. More elaborate extensions of this approach may        include more advanced semantic analyses of the subject content        with its credible and updated/current matching counterparts        whose algorithms are custom configured to confirm (or        alternatively flag) assess the probabilistically estimated        “truthfulness” of contents (where “truthfulness” may be        reassured according to “confirmed with credible source” as well        as scalar measures of degree of likelihood of untruthfulness if        the source is unconfirmed or, for example, exhibits semantically        suspicious inconsistencies with itself, with credible sources or        other patterns which are consistent with fraudulent or deceptive        material.    -   The system may also detect suspicious content, for example, if        its appearance co-occurs in the same message with rogue code        (for example) is co-located (in the same portion of content) as        a macrovirus.        Methods to Repair Post-Attack Damage

In some cases, despite the security, a system in an SDI-SCAM network maybe damaged by an attack. If the attack is completely novel, a humanexpert may be called in to fully analyze the situation and developappropriate repair protocols. These can then be sent to a centralSDI-SCAM damage-control database for use in future situations. In thisway capturing as much data and statistical information regarding theattack and its historical counterpart is valuable both as analysis datafor the human or to enable the system to construct its own optimalrepair protocol.

If an attack method is not novel, the local SDI-SCAM system may accessthis same damage repair database for solutions to the local problem.Among the remedies to damage from an attack: users are alerted,suspicious files are deleted, backup files are loaded, and currentmemory is completely cleared and reloaded with an image from apre-attack state.

FIG. 1: Illustration of Potential Architectures

This FIGURE demonstrates some of the architectural features discussed,including (a) redundant memory within a given machine, (b) redundantconnections between clients and servers, (c) SDI-SCAM installed asprimary security system, (d) SDI-SCAM piggybacking on existing securitysystem, (e) direct client-to-client agent communications, (f) on arouter.

1. A method for employing a distributed network security systempossessing means to detect, prevent and repair device and/or networklevel intrusions.